针对时间序列异常检测，提出自适应模型选择框架，提升跨领域泛化能力。
RAMSeS: Robust and Adaptive Model Selection for Time-Series Anomaly Detection Algorithms
- 用遗传算法优化集成模型，融合多个检测器优势
- 通过强化测试与蒙特卡洛模拟，选出最适合当前数据的单模型
- 适合数据特性多变的场景，尤其对跨领域应用有帮助
时间序列数据在不同领域差异显著，通用异常检测器难以实现。现有方法在某数据集表现优异，但在其他场景常失效，因异常定义具有强上下文依赖性。核心挑战在于设计既能在特定上下文中表现良好，又能适应不同数据复杂度的模型。本文提出鲁棒自适应的时间序列异常检测模型选择框架RAMSeS。该框架包含两个分支：(i) 基于遗传算法优化的堆叠集成，以利用多个检测器的互补性；(ii) 自适应模型选择分支，结合汤普森采样、生成对抗网络的鲁棒性测试及蒙特卡洛模拟，识别最优单个检测器。双策略协同，既发挥多模型集体优势，又适配数据集特征。实验表明，RAMSeS在F1指标上优于现有方法。
原文摘要 · Abstract (English)
Time-series data vary widely across domains, making a universal anomaly detector impractical. Methods that perform well on one dataset often fail to transfer because what counts as an anomaly is context dependent. The key challenge is to design a method that performs well in specific contexts while remaining adaptable across domains with varying data complexities. We present the Robust and Adaptive Model Selection for Time-Series Anomaly Detection RAMSeS framework. RAMSeS comprises two branches: (i) a stacking ensemble optimized with a genetic algorithm to leverage complementary detectors. (ii) An adaptive model-selection branch identifies the best single detector using techniques including Thompson sampling, robustness testing with generative adversarial networks, and Monte Carlo simulations. This dual strategy exploits the collective strength of multiple models and adapts to dataset-specific characteristics. We evaluate RAMSeS and show that it outperforms prior methods on F1.
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